Rate of convergence

In numerical analysis, the speed at which a convergent sequence approaches its limit is called the rate of convergence. Although strictly speaking, a limit does not give information about any finite first part of the sequence, this concept is of practical importance if we deal with a sequence of successive approximations for an iterative method, as then typically fewer iterations are needed to yield a useful approximation if the rate of convergence is higher. This may even make the difference between needing ten or a million iterations.

Similar concepts are used for discretization methods. The solution of the discretized problem converges to the solution of the continuous problem as the grid size goes to zero, and the speed of convergence is one of the factors of the efficiency of the method. However, the terminology in this case is different from the terminology for iterative methods.

We say that this sequence converges linearly to L, if there exists a number μ ∈ (0, 1) such that

The number μ is called the rate of convergence.

If the sequence converges, and

μ = 0, then the sequence is said to converge superlinearly.

μ = 1, then the sequence is said to converge sublinearly.

If the sequence converges sublinearly and additionally

then it is said the sequence {xk} converges logarithmically to L.

The next definition is used to distinguish superlinear rates of convergence. We say that the sequence converges with order q to L for q>1[1] if

In particular, convergence with order

q = 2 is called quadratic convergence,

q = 3 is called cubic convergence,

etc.

This is sometimes called Q-linear convergence, Q-quadratic convergence, etc., to distinguish it from the definition below. The Q stands for "quotient," because the definition uses the quotient between two successive terms.

The drawback of the above definitions is that these do not catch some sequences which still converge reasonably fast, but whose "speed" is variable, such as the sequence {bk} below. Therefore, the definition of rate of convergence is sometimes extended as follows.

Under the new definition, the sequence {xk} converges with at least order q if there exists a sequence {εk} such that

and the sequence {εk} converges to zero with order q according to the above "simple" definition. To distinguish it from that definition, this is sometimes called R-linear convergence, R-quadratic convergence, etc. (with the R standing for "root").

The sequence {ak} converges linearly to 0 with rate 1/2. More generally, the sequence Cμk converges linearly with rate μ if |μ| < 1. The sequence {bk} also converges linearly to 0 with rate 1/2 under the extended definition, but not under the simple definition. The sequence {ck} converges superlinearly. In fact, it is quadratically convergent. Finally, the sequence {dk} converges sublinearly.

A similar situation exists for discretization methods. The important parameter here for the convergence speed is not the iteration number k but it depends on the number of grid points and grid spacing. In this case, the number of grid points n in a discretization process is inversely proportional to the grid spacing.

In this case, a sequence is said to converge to L with order p if there exists a constant C such that

The sequence {dk} with dk = 1 / (k+1) was introduced above. This sequence converges with order 1 according to the convention for discretization methods.

The sequence {ak} with ak = 2−k, which was also introduced above, converges with order p for every number p. It is said to converge exponentially using the convention for discretization methods. However, it only converges linearly (that is, with order 1) using the convention for iterative methods.